Makine Öğrenmesi ve Derin Öğrenme Yöntemleri ile Hidroponik Tarım

Abstract

Günümüzde dünyamızın hızla artan nüfusu karşısında, hızla azalan ham madde ve besin gibi ihtiyaçların karşılanması için araştırmacılar yeni kaynak arayışlarının yanında var olan kaynakları daha etkin ve verimli kullanan çalışmalara da yöneldiler. İnsanlığın en büyük ihtiyaçlarından biri olan besin ihtiyacının karşılanmasında kullanılabilecek alternatif yöntemlerden biri olan hidroponik tarımın kullanımı gün geçtikçe daha popüler hale gelmiştir. Toprak yerine besin solüsyonlu su kullanılması, hava şartlarından etkilenmemesi, kapalı alanlarda uygulanabilmesi ve dikey yönlü olabilmesi hidroponik tarımı diğer tarım yöntemlerinden daha farklı kılan özelliklerdir. Bunun yanında bu tarım yönteminde toprak bulunmaması beraberinde daha çok gözlem ve gözetim ihtiyacını getirmektedir. Bu çalışmanın amacı, hidroponik tarımda verimin artırılması için gerekli olan gözlem ve gözetim ihtiyacının makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak sağlanabileceğini göstermektir. Bu amaçla beş adet makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak yapılan deneysel çalışmalarda hidroponik tarımın verimliliğinin arttırıldığı gözlemlenilmiştir. Derin öğrenme yöntemi %99,7 başarı ile diğer yöntemlere göre daha iyi sonuç elde etmiştir.

Keywords

• Akıllı Tarım
• Makine Öğrenmesi
• Derin Öğrenme
• : Smart Agriculture
• Machine Learning
• Deep Learning

Hydroponic Agriculture with Machine Learning and Deep Learning Methods

Abstract

In the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.

Keywords

• Akıllı Tarım
• Makine Öğrenmesi
• Derin Öğrenme
• : Smart Agriculture
• Machine Learning
• Deep Learning
• : Smart Agriculture, Machine Learning, Deep Learning

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